The proposed MM-RealSR constructs a conditional mapping in real-world super-resolution. The training objective is to drive our MM-RealSR to match this mapping relation, leading to two specific goals, i.e., interactive modulation mechanism and GAN-based image restoration.
The interactive modulation mechanism is optimized with the metric learning strategy, as illustrated in Sec. REF , including two margin ranking losses (\(\mathcal {L}_{ML}^{n}\) , \(\mathcal {L}_{ML}^{b}\) ) and two anchor losses (\(\mathcal {L}_{AC}^{u}\) , \(\mathcal {L}_{AC}^{l}\) ).
The GAN-based image restoration is optimized with \(\mathcal {L}_{1}\)  loss, perceptual loss \(\mathcal {L}_{per}\)  [1]}, and GAN loss \(\mathcal {L}_{GAN}\)  [2]}.
The total training objective is defined as:
\(\mathcal {L} = \lambda _1 \mathcal {L}_{GAN} + \lambda _2 \mathcal {L}_{per} + \lambda _3 \mathcal {L}_{1} + \lambda _4(\mathcal {L}_{ML}^{n} + \mathcal {L}_{ML}^{b} + \mathcal {L}_{AC}^{u} + \mathcal {L}_{AC}^{l}),\) 
